1 Introduction

The objective of this notebook is to cluster cells at a low resolution that allows us to “fetch” the clusters that are potential doublets, so that we can easily exclude them.

2 Pre-processing

2.1 Load packages

library(Seurat)
library(tidyverse)
library(ggpubr)
library(reshape2)

2.2 Parameters

# Paths
path_to_obj <- str_c(
  here::here("scATAC-seq/results/R_objects/level_2/"),
  params$cell_type,
  "/",
  params$cell_type,
  "_integrated_level_2.rds",
  sep = ""
)

path_to_doublets <- here::here("scRNA-seq/3-clustering/2-level_2/tmp/doublets_multiome_df_all.rds")

# Functions
source(here::here("scRNA-seq/bin/utils.R"))


# Colors
color_palette <- c("black", "gray", "red", "yellow", "violet", "green4",
                   "blue", "chocolate1", "coral2", "blueviolet",
                   "brown1", "darkmagenta", "deepskyblue1", "dimgray",
                   "deeppink1", "green", "lightgray", "hotpink1",
                   "indianred4", "khaki", "mediumorchid2", "gold", "gray")

2.3 Load data

# Seurat object
seurat <- readRDS(path_to_obj)
seurat
## An object of class Seurat 
## 270784 features across 27497 samples within 1 assay 
## Active assay: peaks_macs (270784 features, 267464 variable features)
##  3 dimensional reductions calculated: umap, lsi, harmony

3 Cluster

resolutions <- c(0.01, 0.025, 0.05, 0.1)
seurat <- FindClusters(seurat, resolution = resolutions)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 27497
## Number of edges: 810353
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9900
## Number of communities: 4
## Elapsed time: 5 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 27497
## Number of edges: 810353
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9752
## Number of communities: 5
## Elapsed time: 4 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 27497
## Number of edges: 810353
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9560
## Number of communities: 5
## Elapsed time: 4 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 27497
## Number of edges: 810353
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9249
## Number of communities: 7
## Elapsed time: 4 seconds
vars <- str_c("peaks_macs_snn_res.", resolutions)
umap_clusters <- purrr::map(vars, function(x) {
  p <- DimPlot(seurat, group.by = x, cols = color_palette)
  p
})
umap_clusters
## [[1]]

## 
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## 
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## 
## [[4]]

clusters_assay_specific <- purrr::map(vars, function(x) {
  data <- table(seurat@meta.data[,x], seurat@meta.data$assay)
  data.perc <- apply(data, 1, function(x){x/sum(x)})
  data.perc_melt <- melt(data.perc)
  data.perc_melt$Var2 <- as.factor(data.perc_melt$Var2)
  data.perc_melt$value <- round(data.perc_melt$value,2)
  
  p <- ggbarplot(data.perc_melt, "Var2", "value",
  fill = "Var1", color = "Var1", 
  label = TRUE, lab.col = "white", lab.pos = "in")
  p + scale_fill_manual(values=c("#E69F00", "#56B4E9"))
})
clusters_assay_specific
## [[1]]

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## 
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## 
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4 Proportion of doublets per cluster based on Scrublet

doublet_clusters <- purrr::map(vars, function(x) {
  df1 <- data.frame(table(seurat@meta.data[,x], seurat@meta.data$scrublet_predicted_doublet_atac))
  colnames(df1) <- c("Cluster", "Scrublet","Cells")
  p <- ggbarplot(df1, "Cluster", "Cells",
  fill = "Scrublet", color = "Scrublet",
  label = TRUE,
  position = position_dodge(0.9))
  p
})
doublet_clusters
## [[1]]

## 
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## 
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4.1 UMAP level 1

umap_clusters_level1 <- purrr::map(vars, function(x) {
  p <- FeatureScatter(seurat, 
                      "UMAP_1_level_1",
                      "UMAP_2_level_1", group.by = x, cols = color_palette)
  p
})
umap_clusters_level1
## [[1]]

## 
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## 
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## 
## [[4]]

5 Session Information

sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS/LAPACK: /Users/pauli/opt/anaconda3/envs/Tonsil_atlas/lib/libopenblasp-r0.3.10.dylib
## 
## locale:
## [1] es_ES.UTF-8/UTF-8/es_ES.UTF-8/C/es_ES.UTF-8/es_ES.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] Signac_1.1.0.9000 reshape2_1.4.4    ggpubr_0.4.0      forcats_0.5.0     stringr_1.4.0     dplyr_1.0.2       purrr_0.3.4       readr_1.4.0       tidyr_1.1.2       tibble_3.0.4      ggplot2_3.3.2     tidyverse_1.3.0   Seurat_3.9.9.9010 BiocStyle_2.16.1 
## 
## loaded via a namespace (and not attached):
##   [1] rappdirs_0.3.1              SnowballC_0.7.0             rtracklayer_1.48.0          GGally_2.0.0                bit64_4.0.5                 knitr_1.30                  irlba_2.3.3                 DelayedArray_0.14.0         data.table_1.13.2           rpart_4.1-15                RCurl_1.98-1.2              AnnotationFilter_1.12.0     generics_0.1.0              BiocGenerics_0.34.0         GenomicFeatures_1.40.1      cowplot_1.1.0               RSQLite_2.2.1               RANN_2.6.1                  future_1.20.1               bit_4.0.4                   spatstat.data_2.1-0         xml2_1.3.2                  lubridate_1.7.9             httpuv_1.5.4                SummarizedExperiment_1.18.1 assertthat_0.2.1            xfun_0.18                   hms_0.5.3                   evaluate_0.14               promises_1.1.1              fansi_0.4.1                 progress_1.2.2              dbplyr_1.4.4                readxl_1.3.1                igraph_1.2.6                DBI_1.1.0                   htmlwidgets_1.5.2           reshape_0.8.8               stats4_4.0.3                ellipsis_0.3.1              backports_1.2.0             bookdown_0.21              
##  [43] biomaRt_2.44.4              deldir_0.2-3                vctrs_0.3.4                 Biobase_2.48.0              here_1.0.1                  ensembldb_2.12.1            ROCR_1.0-11                 abind_1.4-5                 withr_2.3.0                 ggforce_0.3.2               BSgenome_1.56.0             checkmate_2.0.0             sctransform_0.3.1           GenomicAlignments_1.24.0    prettyunits_1.1.1           goftest_1.2-2               cluster_2.1.0               lazyeval_0.2.2              crayon_1.3.4                labeling_0.4.2              pkgconfig_2.0.3             tweenr_1.0.1                GenomeInfoDb_1.24.0         nlme_3.1-150                ProtGenerics_1.20.0         nnet_7.3-14                 rlang_0.4.11                globals_0.13.1              lifecycle_0.2.0             miniUI_0.1.1.1              BiocFileCache_1.12.1        modelr_0.1.8                rsvd_1.0.3                  dichromat_2.0-0             cellranger_1.1.0            rprojroot_2.0.2             polyclip_1.10-0             matrixStats_0.57.0          lmtest_0.9-38               graph_1.66.0                ggseqlogo_0.1               Matrix_1.3-2               
##  [85] carData_3.0-4               zoo_1.8-8                   reprex_0.3.0                base64enc_0.1-3             ggridges_0.5.2              png_0.1-7                   viridisLite_0.3.0           bitops_1.0-6                KernSmooth_2.23-17          Biostrings_2.56.0           blob_1.2.1                  parallelly_1.21.0           jpeg_0.1-8.1                rstatix_0.6.0               S4Vectors_0.26.0            ggsignif_0.6.0              scales_1.1.1                memoise_1.1.0               magrittr_1.5                plyr_1.8.6                  ica_1.0-2                   zlibbioc_1.34.0             compiler_4.0.3              RColorBrewer_1.1-2          fitdistrplus_1.1-1          Rsamtools_2.4.0             cli_2.1.0                   XVector_0.28.0              listenv_0.8.0               patchwork_1.1.0             pbapply_1.4-3               ps_1.4.0                    htmlTable_2.1.0             Formula_1.2-4               MASS_7.3-53                 mgcv_1.8-33                 tidyselect_1.1.0            stringi_1.5.3               yaml_2.2.1                  askpass_1.1                 latticeExtra_0.6-29         ggrepel_0.8.2              
## [127] grid_4.0.3                  VariantAnnotation_1.34.0    fastmatch_1.1-0             tools_4.0.3                 future.apply_1.6.0          parallel_4.0.3              rio_0.5.16                  rstudioapi_0.12             lsa_0.73.2                  foreign_0.8-80              gridExtra_2.3               farver_2.0.3                Rtsne_0.15                  digest_0.6.27               BiocManager_1.30.10         shiny_1.5.0                 Rcpp_1.0.5                  GenomicRanges_1.40.0        car_3.0-10                  broom_0.7.2                 later_1.1.0.1               RcppAnnoy_0.0.16            OrganismDbi_1.30.0          httr_1.4.2                  AnnotationDbi_1.50.3        ggbio_1.36.0                biovizBase_1.36.0           colorspace_2.0-0            rvest_0.3.6                 XML_3.99-0.3                fs_1.5.0                    tensor_1.5                  reticulate_1.18             IRanges_2.22.1              splines_4.0.3               RBGL_1.64.0                 uwot_0.1.8.9001             RcppRoll_0.3.0              spatstat.utils_2.1-0        plotly_4.9.2.1              xtable_1.8-4                jsonlite_1.7.1             
## [169] spatstat_1.64-1             R6_2.5.0                    Hmisc_4.4-1                 pillar_1.4.6                htmltools_0.5.1.1           mime_0.9                    glue_1.4.2                  fastmap_1.0.1               BiocParallel_1.22.0         codetools_0.2-17            lattice_0.20-41             curl_4.3                    leiden_0.3.5                zip_2.1.1                   openxlsx_4.2.3              openssl_1.4.3               survival_3.2-7              rmarkdown_2.5               munsell_0.5.0               GenomeInfoDbData_1.2.3      haven_2.3.1                 gtable_0.3.0